# Amazon — How Indegene is modernizing and scaling enterprise data quality in life sciences

- Company: Amazon (amazon.com)
- Announced: 2026-11-04T16:25:57+00:00
- Category: not stated
- Coverage: not counted
- Announcement: no
- Group: routine
- Source: https://aws.amazon.com/blogs/machine-learning/how-indegene-is-modernizing-and-scaling-enterprise-data-quality-in-life-sciences/
- Record: https://forck.live/items/31632-how-indegene-is-modernizing-and-scaling-enterprise-data-quality-in-life-sciences
- Subject: Bedrock / Nova

Indegene built an LLM-powered Data Quality Agent inside its NEXT Unified Data Platform on AWS to generate, validate and code data quality rules for life sciences clients. The company says manual rule creation, fragmented governance and shifting business definitions make data quality hard to scale across enterprise data ecosystems. The agent produces context-aware rules and ready-to-run SQL and Python code under centralized, human-in-the-loop governance, according to the post. Indegene describes NEXT UDP as an AI-driven platform that consolidates commercial, medical, clinical and enterprise data into one governed environment spanning ingestion to analytics. The platform ships standalone agents for data ingestion, data quality, orchestration, data catalog and SQL bot generation, which can run in pipelines or be customized for agent-to-agent communication. Indegene says it serves the world's top 20 biopharma companies and works across commercial, medical, R&D and safety operations.

## Evidence

Verbatim from https://aws.amazon.com/blogs/machine-learning/how-indegene-is-modernizing-and-scaling-enterprise-data-quality-in-life-sciences/:

> This post is co-written with Shravan K S from Indegene Limited.
> 
> Enterprise AI is creating new opportunities for life sciences organizations to improve decision-making across commercial, medical, market access, and other business functions. But the quality of every AI model, dashboard, and business insight depends on one thing: trusted data. As enterprise data grows across multiple platforms, pipelines, and business domains, maintaining consistent data quality has become increasingly difficult. Manual data quality rule creation, fragmented governance, and evolving business definitions make it challenging to scale data quality across the enterprise.
> 
> This post explores how Indegene built a production-grade, large language model (LLM) powered Data Quality Agent within its NEXT Unified Data Platform (NEXT UDP) on AWS. The agent generates context-aware data quality rules, validates them, and produces ready-to-run SQL and Python code under centralized, human-in-the-loop governance. Together, these capabilities help organizations improve data quality at enterprise scale while reducing the effort required to build and maintain data quality programs.
> 
> About Indegene

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Record: https://forck.live/items/31632-how-indegene-is-modernizing-and-scaling-enterprise-data-quality-in-life-sciences
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